Early Crop Identification Study Based on Sentinel-1/2 Images with Feature Optimization Strategy

作者：Jiansong Luo, Min Xie, Qiang Wu, Jun Luo, Qi Gao, Xuezhi Shao, Yongping Zhang · 发表于：Agriculture · 年份：2024 · DOI：10.3390/agriculture14070990 · 被引用次数：11 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Remote Sensing and Land Use

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reac...